Name
Title | ||
|---|---|---|
Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection |
Abstract | ||
|---|---|---|
In this paper, we present a real-time automatic vision-based rail inspection system, which performs inspections at 16 km/h with a frame rate of 20 fps. The system robustly detects important rail components such as ties, tie plates, and anchors, with high accuracy and efficiency. To achieve this goal, we first develop a set of image and video analytics and then propose a novel global optimization framework to combine evidence from multiple cameras, Global Positioning System, and distance measurement instrument to further improve the detection performance. Moreover, as the anchor is an important type of rail fastener, we have thus advanced the effort to detect anchor exceptions, which includes assessing the anchor conditions at the tie level and identifying anchor pattern exceptions at the compliance level. Quantitative analysis performed on a large video data set captured with different track and lighting conditions, as well as on a real-time field test, has demonstrated very encouraging performance on both rail component detection and anchor exception detection. Specifically, an average of 94.67% precision and 93% recall rate has been achieved for detecting all three rail components, and a 100% detection rate is achieved for compliance-level anchor exception with three false positives per hour. To our best knowledge, our system is the first to address and solve both component and exception detection problems in this rail inspection area. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TITS.2013.2287155 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
railroad track inspection,ties,optimisation,distance measurement,global positioning system,anchors,video signal processing,machine vision technology,rail component optimization,engineering computing,video data set,rail component assessment,compliance-level anchor exception,automatic rail track inspection,video cameras,fasteners,real-time automatic vision-based rail inspection system,distance measurement instrument,plates (structures),multisensor evidence integration,automatic optical inspection,rail component detection,image analytics,exception detection problems,railway safety,computer vision,rail fastener,video analytics,multiple cameras,railway engineering,real-time systems,anchor exception detection,tie plates,machine vision,inspection,lighting,railroad tracks,real time systems,optimization | Rail inspection,Computer vision,Railway engineering,Global optimization,Machine vision,Simulation,Track (rail transport),Global Positioning System,Frame rate,Artificial intelligence,Engineering,False positive paradox | Journal |
Volume | Issue | ISSN |
15 | 2 | 1524-9050 |
Citations | PageRank | References |
6 | 0.59 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Li | 1 | 175 | 19.80 |
| Hoang Trinh | 2 | 49 | 7.09 |
| Norman Haas | 3 | 100 | 46.34 |
| Charles Otto | 4 | 6 | 0.59 |
| Sharath Pankanti | 5 | 3542 | 292.65 |